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The process of automatically masking objects from complex backgrounds is extremely beneficial when trying to utilize those objects for computer vision research, such as object detection, autonomous driving, pedestrian tracking, etc. Therefore, a robust method of segmentation is imperative towards ongoing research between the Digital Imaging and Remote Sensing Laboratory at the Rochester Institute of Technology and the Savannah River National Laboratory directed at the volume estimation of condense water vapor plumes emanating from mechanical draft cooling towers. Instance segmentation was performed on a custom data set consisting of RGB imagery with the Matterport Mask R-CNN implementation,1 where condensed water vapor plumes were masked out from mixed backgrounds for the purpose of 3D reconstruction and volume estimation. This multi-class Mask R-CNN was trained to detect cooling tower structure and plumes with and without data augmentation to study the effects on a preliminary data set, in addition to a model trained with a single plume class. The average precision and intersection over union metrics across all models were shown to not be statistically different. While each model is capable of detecting and segmenting plumes in the preliminary data set, all models essentially perform the task with the same efficacy. This indicates some level of bias in the preliminary data set, demonstrating the need for more variance in the form of additional annotated imagery. The single plume class model tested within 7% for mAP, AP, and IoU when compared to the other two models, demonstrating the ability of Mask R-CNN to detect and segment these dynamically-changing plumes without any spatial dependence on the stationary cooling tower structure. This ongoing research includes a long-term data collection campaign where imagery of condensed water vapor plumes will be continuously gathered over an 18-month period so as to include imagery examples under many different meteorological and environmental conditions, seasonal variations, and illumination changes that will occur over an annual cycle. Including this data in future training of the Mask R-CNN implementation is expected to reduce any bias that may exist in the current data set.
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